Tag: Personalization

Direct Mail Marketing Predictions for 2016

Direct mail is not dead, far from it. With the growth in technology and personalization, direct mail has become an even more powerful player for your ROI. But 2016 will bring tightened direct mail budgets, since marketers need to spread their money farther and farther. Because of that, serious considerations need to be made

Crystal ball and the horizonDirect mail is not dead, far from it. With the growth in technology and personalization, direct mail has become an even more powerful player for your ROI. But 2016 will bring tightened direct mail budgets, since marketers need to spread their money farther and farther. Because of that, serious considerations need to be made with direct mail.

The first, of course, is how to improve your ROI. So far the only postage increases announced have been for packages, but we will need to be on the lookout for possible changes in late spring 2016. If postage rates remain the same, that can help to increase your ROI. The post office will also be finishing consolidation which may increase delivery times like we saw last spring. So timing will need to be considered as well.

Here are my five predictions for 2016, so can you continue to grow your direct mail marketing ROI.

  1. Personalized: We will see an increase in customers expecting direct mail that is relevant to them. Offers based on what they want and need are going to be the key to your success. The mail boxes are no longer full of junk, but useful offers they can take advantage of.
  2. Integration: When integrating other channels with direct mail, you create more engaging content and leave a better impression on customers and prospects. The longer you can keep their attention, the greater chance you have to capture the sale. There are many cost effective options you can use to integrate from mobile and email to online content.
  3. Multilayered: 2016 will bring more multilayered direct mail campaigns. The staggering of different pieces going at selected intervals continually. Since, as we know, direct mail is more effective the more often someone gets your offer, this will help increase your ROI.
  4. Enhance your database: Constantly adding information into your database is very important. The more you know about each customer the better able to you are to target them. If you are just starting out and need a little help, you can profile your customer database to find out more information about them. Ask your mail service provider how.
  5. Multiple response devices: The more options you have for people to respond to your offer, the better response you are going to get. So for 2016, add in a new response method. That can be a wide range from QR Codes to texting a response code. Find what you think your customers/prospects are most interested in and try it out.

Consumers are smarter and expect more from companies now. They feel powerful and in the driver’s seat of their experiences with you. In order to compete, you will need to meet or exceed those expectations. Your brand needs to be approachable and knowledgeable about each person. The more information you have in your database about each person the better you can target your messages and offers.

Direct mail can be a very creative way to reach people who are interested in your product or service. Think of all the fun ways you can go beyond imagery to engage people. Having fun with your creativity can be a real boost to your ROI. Make sure you consult with your mail service provider about the design of your piece to make sure that you are not creating a postal problem, which will cost you a lot of money. Let’s make 2016 the best year ever for direct mail marketing!

 

Barriers to Personalization

Recently, I stumbled onto survey results from marketers regarding “data-related headaches,” published by a reputable source. What surprised me the most was not the list of the pain points, but the way marketers expressed the severity of pains. They collectively answered that “moving data among different silos” and “gaining a

Recently, I stumbled onto survey results from marketers regarding “data-related headaches,” published by a reputable source. What surprised me the most was not the list of the pain points, but the way marketers expressed the severity of pains. They collectively answered that “moving data among different silos” and “gaining a single customer view” gave them the most severe headaches, while “personalization” brought not-so-severe pain. That gave me an “oh, really?” moment. Then they put “contextualization” (of data, I assume) and “turning data into insights, and insights into actionable segments” right in the middle.

From a data and analytics specialist’s point of view, it seems like marketers have no idea where the pains originated. Simply, proper personalization is not possible without the 360-degree view of a customer and insights derived from the data. So, in my opinion, the severity list seems completely backward. And it is just unbelievable that marketers “think” that they are performing some type of personalization without much of a headache.

During the past few months, I have been emphasizing the importance of personalization in modern marketing (refer to “Personalization Is About the Person”), and data and analytical steps to achieve such goals (refer to “Road to Personalization”). I’ve said similar lines many times before, but let me repeat: Proper personalization is not possible without understanding the target individuals as people. If marketers are thinking that buying some fancy software and putting transaction- and event-level data through it are the end of their jobs, they cannot be more wrong. Such activity often leads to “personally annoying people,” not impressing customers with relevant messages. If they were to automate such a rudimentary practice? Well, they are going to end up annoying their customers and prospects on a regular basis.

If you as a marketer are having a hard time stomaching what I am saying here, please then take a look at your inbox, which is filled with irrelevant messages — as it is for a consumer. Aren’t they filled with the kinds that you would purge mercilessly, as in “highlight all, then delete”? How many messages are really relevant and timely to you? Maybe one out of 300 to 400? Even the ones that are based on some tidbits of information that you left behind purposefully or accidently become really annoying after the third time you see the same darn message stemming from them. Ok, I get that some marketers think that they know me, but could they please not overdo it by turning on some expensive personalization engine on an autopilot mode from day one?

As I emphasized in my previous columns, personalization is about the person. Putting event-level or transaction data into a personalization engine is like putting unrefined oil into a high-performance engine. Not a recommended course of action, for sure. And don’t blame the engineer when things break down, even though the salesperson who sold you that engine probably claimed that it would make all of your marketing dreams come true.

Regardless, I think we can safely agree that personalization must start with the data. Unfortunately, not all data are created equal or are of the same quality (refer to “Chicken or the Egg? Data or Analytics?”). In fact, most data are utterly inadequate for high-level personalization that does not annoy people. So yes, the fact that marketers think that creating a single customer view out of all types of data from different silos is indeed important and difficult is a good sign. A critical change always starts with the recognition of a problem. It is just that marketers should never think that personalization engines could magically help them skip that painful step of data hygiene and consolidation.

If the data management were the first hurdle on the way to decent personalization, then the second challenge that marketers often face would be the analytical part of the journey. Deriving insights out of data and turning such insights into actionable segments require advanced-level analytical skills. Here again, automated machines do not perform the human part of the equation. Some marketers may have procured some automated modeling engine (again, with much fanfare as a magical tool). But who will set the goals of models and define the target for each model (refer to “Data Deep Dive: The Art of Targeting”)? Who will connect the dots between resultant personas and segments to actual offers and messages that customers and prospects get to see?

Even for cases where marketers must respond to a customer’s need immediately (e.g., for buyers who are specifically looking for a specific product right now), the rules of engagement (i.e., customer journey mapping) must be set up based on clear business objectives, as well as mathematical equations. Humans, not so surprisingly, can smell the sign of not-humanness from miles away, through even digital channels.

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Road to Personalization

The marketing community loves buzzwords. One may say that some words just go viral. In the past, CRM was one. Server-migration (from mainframes) was another. Cloud computing – even among non-IT groups – has some magic power. Big Data has indeed been a big one the past few years, though

The marketing community loves buzzwords. One may say that some words just go viral. In the past, CRM was one. Server-migration (from mainframes) was another. Cloud computing – even among non-IT groups – has some magic power. Big Data has indeed been a big one the past few years, though it surely is losing its coolness, especially among data professionals. But, in some countries and communities, it is still gaining momentum. The latest one, I think, is “Personalization.”

Do you know how I get to find out how some words are becoming popular? The fastest way is to attend a conference and check out which session keywords are filling up the rooms. Attendance, like in the movie industry, is a sure way to measure the power of the keyword. We often see that some speeches and articles are not even remotely related to the word in question, but that doesn’t seem matter much. Everyone and their cousins start selling the word like it’s a magic potion that cures all. If you happen to come across a password to a goldmine, won’t you try it, too?

Once the word starts go viral, the power of magic starts to influence the real-life decision-making processes. Yes, I’ve been using every chance to debunk the mystery around Big Data, through this series and other opportunities. But I have to admit that those two words originally strung together for marketing purposes by software companies opened so many new doors to meetings and speaking engagements to which data geeks never dreamed of having access a mere four to five years ago. If you ask me what the best outcome of the Big Data movement is, my answer is that decision-makers, in general, became aware of the importance of analytics based on collected data. Analysts no longer have to spend a long time in meetings to justify the usage of data and analytics; we can simply dive right into the subject now.

Nevertheless, I still have a strong allergic reaction to buzzwords, like I do to syrupy pop songs of which I tire easily. The main reason, other than I just get sick of hearing them, is because buzzwords lead to kingdom-come-level promises. Overpromises lead to overinvestments, which lead to equally big disappointments (try showing decent ROI on overinvestments), which inevitably lead to finger-pointing and blame-games. That is why I, over time, tried to isolate the beneficial elements of Big Data, and attempted to put different spins and labels on it (refer to “Big Data Must Get Smaller” and “Smart Data, not Big Data”). After all, I am a believer in data and analytics for real-life (i.e., not theoretical) applications, and I want decision-makers and marketers to succeed. I want to find ways to make money with data, whatever you name that activity.

Now I see that the word “Personalization” is becoming the hot topic in conference circuits and the blogosphere. More and more, that word is uttered even during the first encounter with a potential client. Signs are everywhere that it is about to be “the” buzzword in the marketing community.

And I welcome it. Through this series, I have been repeating that the key goals of analytical activities for marketers, regardless of employed channels, should be:

  • Knowing whom to contact, and
  • Knowing what to offer through what channel, if a customer or prospect is indeed to be contacted.

An amazing amount of data that became available to marketers led to over-communication to an “everyone, all the time” level, and the response rate of any marketing endeavor cannot be sustained that way. Out of desperation, some marketers actually “increase” the contact frequency to maintain the revenue level, and some already have reached a “six times a week, per target” level. What are they going to do after reaching seven times per week? What then? Invent a new day, like Ringo Starr blurted out with “8 days a week”? Spamming more surely isn’t the way out.

Some of my colleagues ask me if we should just take a leap of faith that personalization is the key to the future of marketing, as there aren’t many – there are only few – good success stories about it yet. My answer is to look at all these marketing messages from the consumer’s point of view. Aren’t you completely sick of this barrage of irrelevant pushes, even from so-called reputable retailers? Wouldn’t you pay more attention to something that is more relevant to you, that resonates with you over countless inept and, at times, completely annoying messages? When we show a group photo to anyone, most people check themselves in the picture first. How do “I” look in it? Let’s face it, everyone cares about themselves first, and we are conditioned to pick out anything about us through all kinds of noises.

That is why I believe that this personalization is the future of marketing. In the age of information overload, it is the customers who are picking and choosing messages that are relevant to them, not the other way around. Everyone is exposed to at least five to six types of screens every day. And with new inventions, the noise level will certainly increase. We are no longer living in the world where marketers can just push the products and services according to their priorities. Instead, consumers are ranking products and services. Traditional “push”-type endeavors still have their place in marketing. But in the future, “every” channel will be a 1-to-1 medium, and the consumers will be in full control, choosing what they want to see and mercilessly ignoring irrelevant messages. Marketers must try their best to comply to that demand and show consumers what they may like to see, using all available data and statistical techniques. And the marketers do that right will move ahead. But only if they do it right (refer to “Personalization is about the Person”).

The road to proper personalization is a long and winding one. It starts with the data, of course, as we need to decide “who gets what message” based on them. Various technologies must be employed to display different versions of messages through multiple channels individually, still maintaining consistency. Multiple versions of copies should be written and new stack of creatives must be prepared. Collected data should be refined to be used in such personalization engines, as raw data can only do so much, even with very expensive toolsets. If required data are not explicit enough, or worse, not available at all, we will need to calculate the propensity of certain desired behaviors or consumer characteristics – as in, “not sure if the target is a health-conscious young parent for certain, but he surely looks like one.” As I stated in my previous columns, explicit data are hard to come by, even in the age of Big Data, and we all must make the most of what we get to have. No customer will wait until you have the perfect set of data.

Like in any field, may it be a musical field or martial arts, there are virtuosos (or “virtuosi”?) and grand-masters, then there are mediocre talents and complete novices. In data and analytics such levels exist, as well. Not all analysts or data scientists are on the same level, though I often argue that an unexceptional statistical model is still better than someone’s gut feeling. For end-to-end marketing executions, things get more complicated, as many different types of technologies and skills, as well as overall vision, must work harmoniously to achieve goals. Unfortunately, I often see marketers who still don’t believe in the effectiveness of advanced analytics because they “think” that they had a bad experience with it. But is it fair to dismiss time-tested methods, when many other factors could have gone wrong?

In the interest of not killing the idea of “Personalization” due to unfavorable results from rudimentary trials, allow me to share the “10 stages of personalization efforts” from a data, analytics and technology point of view (i.e., marketing creative is not considered here):

  • Not even considering personalization yet. They still think that spraying the same HTML to everyone is alright, as long as the process runs smoothly.
  • Personalization is considered, but they do not know where to start.
  • Identified basic steps toward personalization, but they do not have specific data or a technology roadmap.
  • Created the data roadmap, but they did not start thorough data inventory.
  • Identified required data sources, but datasets are not cleaned up or consolidated for 360-degree view of customers (a must-have in personalization).
  • Datasets are ready for personalization, but only with “known” (or explicit) data; statistical modeling to fill in the gaps is not considered yet.
  • Tested personalization engines through major marketing channels of choice, employing collected “known” (or explicit) data.
  • Creating “personas” (or implicit data) using statistical techniques with available data, filling in the gaps with statistical models (an ongoing effort).
  • Personalizing most messages and offers through every touchpoint, employing explicit data (known data) and implicit/inferred data (in forms of personas).
  • Collecting and utilizing results data to enhance model-based personas and personalization engines continuously, leading to automation.

So, at what stage is your organization? Are supporting datasets previously locked in channel silos merged together to form a customer-centric view? Or are you just plugging transaction or event-level data into some personalization software with a fancy name and a high price tag? Are you personalizing only sometimes through some channels to some people who happened to volunteer – explicitly or implicitly – some of their information to you, or are you doing it for most people, most times, through most channels? The differences are huge. Unfortunately, too many marketers are just personally annoying customers in the name of personalization, and most don’t even do that consistently.

I understand that not all marketing organizations have to achieve ninth-degree black belts in personalization, as from company to company, business models, channel usage, success metrics, budget limitations and available data are undeniably different. Nevertheless, I dare to say that personalization will be more important for the survival of most businesses, as companies that are better at it are visibly leaping ahead. Look at the ways that some big name retailers are doing it from a consumer’s perspective; they are clearly not operating under the old paradigm of “marketers push, consumers respond.” Even when committed to the concept, before any organization gets into the thick of things, decision-makers must set the data and technology roadmap first. The order of operation is important here, and it would be easier to prove the worthiness of the endeavor in baby steps, too. Dismissing the whole idea after trying a few rudimentary steps out of order would be a real shame.

Like any guru would say, awareness is the first step toward improvement. Understanding how far one must go is at the core of any learning process. Isn’t that what Master Yoda tried to teach a young Jedi named Luke Skywalker on that swampy planet of Dagobah?

Personalization Is About the Person

Personalization is about the person. Some may say that I am stating the obvious. Really? Then explain this.

Personalization is about the person. Some may say that I am stating the obvious. Really? Then explain this.

Some time ago, I bought a new garden hose nozzle for my wife, as she took up a new hobby of cultivating vegetables that we actually consume. The last time I bought such an item was more than 15 years ago, so I did some online research (but don’t automatically label me as an “online” person yet). After a few clicks, I ended up on a good old Amazon site, and bought what I would call the Cadillac of garden hose nozzles. Not only did it come with all kinds of options, intuitive handle and switches, it also was in Ferrari red. All in all, I had a very positive shopping experience.

Then a not-so-great customer experience started to happen. OK, it was still kind of cute that they showed “more of the same” items after I checked out. It is entirely possible that I’d find something even nicer seconds after the transaction, and cancel the original order and buy an alternate item. What was really annoying was that this almighty Amazon started to send a series of emails only featuring, well, guess what, more garden hose nozzles! Ah, maybe they pinned me as a “gardening enthusiast” with some fancy algorithm? Or they thought that I started a wholesale business selling nothing but fancy nozzles? Or I just became a collector of nozzles without realizing it? Then again, was I giving too much credit to their analysts?

The answer revealed itself through my next purchase with them just a few weeks later. Like many modern households, we have lots of computers scattered around our house (although not all of them could be networked together, thanks to not-so-intuitive Microsoft operating systems). One of the older computers was still with a seriously outdated mouse, so I decided to replace it with a newer model that I tried and liked. That means I needed zero research time for that purchase. Just log onto Amazon, type in the brand and model number, and do that famous one-click checkout. Simple, right?

Guess what happened after that purchase? I started to receive a new series of emails from them, this time featuring nothing but more mice! What do they think, I love this mouse so much that I would start a mouse farm? Do they want me to find a better mouse “after” I purchased one already? The “last” thing I would buy for the rest of this fiscal year is another mouse (and another garden hose nozzle, if I might add). I cannot forgive their oversight, because I bought the first mouse with the same merchant, only about 16 months ago. Don’t they have my personal transaction history? For heaven’s sake, I can just log onto Amazon.com and check out what I have been buying from it going back almost 10 years! Why don’t they use such rich data? Isn’t Amazon supposed to be one of the leading database marketers?

How did this all happen? I have two words for you: “Collaborative Filtering,” though I have no idea what they are really collaborating in cases like these. That term has been around for some time now, actually. It basically means, “Oh, you bought this item? You may like these other products, too”-type marketing through some algorithm. Now I know that when we use terms like “algorithmic solution,” we may feel a little smarter about ourselves — as in, “Yeah! I am not afraid of math!” But let’s forget about how the math works, and let’s think about how the consumers feel about it.

If I may share my blunt sentiment about this type of suggestion engine, my language would have to be more colorful than this fine publication allows me to be. Let’s just say that I am pretty far from impressed. And marketers should not even think about calling this barrage of emails “personalized.” They are much closer to spam than personalized emails, because I know this type of personalization is based on products, not people. And this so-called “machine learning” becomes nothing more than a nuisance, if the all-important human touch is missing from the equation.

Clearly, “personalization” is the buzzword du jour, actively pushing “Big Data” out of its short-lived glory. We can see that trend at conferences, marketing meetings, industry papers and blogs like this. And unlike other buzzwords that came and went in the marketing industry, I am boldly predicting that this “personalization” is here to stay for a foreseeable time. Why? Because consumers demand it, they feel that they are entitled to it, and marketers finally have the technology and data at their command to do it. But alas, only if they do it right. Unfortunately, I see a lot of marketers – even so-called leading online marketers – just personally annoying the heck out of their customers. As a result, it is very difficult to find good success stories about personalization. This, even as everyone demands case studies (as if they won’t commit to it, unless others succeed in that endeavor first).

So, I ask marketers this question: Are you really committed to do it, or are you just saying that word simply because it is a new thing to do now? I ask for commitment as an organization, because doing it right requires a lot more than just purchasing a personalized engine off the shelf.

Recently, I attended an eTailer conference and happened to sit next to a digital marketer at a networking lunch (i.e., a free piece of meat with a salad). When I tried to explain what I do for personalization efforts, she stopped me and said, “Oh, personalization engines do that for us.” Really? To me, that sounds a lot like saying that coffee comes out of an espresso machine. I didn’t say anything at the time (I was busy chewing the meat), but I believe that such a myopic view is the main cause for all of those rudimentary and ineffective personalizations. For you to enjoy that cup of coffee ever so conveniently, someone had to cultivate coffee plants (I bet without any fancy hose nozzle), harvest the beans, process them, transport them, do all the paperwork to go through customs, domestically distribute them, roast them to various degrees and package them for espresso machines. Likewise, for personalization engines to function properly, incoming data must go through some serious refinement process.

Without a doubt, proper personalization starts with a personalized data view, which is skipped over all too often. Some may use terms like “360-degree customer view,” “single customer view” or my favorite, “customer-centric portrait.” No matter. All the transactional, behavioral, demographic and environmental data must be realigned around “each” customer or prospect. Some may say that they already have some fancy ID system that connects all those data points (many don’t). Great, but that is just a good beginning. We still need to convert such “event”-level data into “descriptors” of individuals. Transaction-level data may tell you what happened on a certain date, for how much money and for what product. Descriptors of individuals display their personal spending patterns, such as personal compositions of categorical purchases and browsing history, frequency of purchases and spending levels in each category or channel, and sets of times series variables nicely lined up around the person (refer to “Beyond RFM Data”). This is quite different from stacks of transaction or event-level data sitting in data platforms designed for mass storage and rapid retrieval.

When we line up information around people, we often find out that we really do not know much about our customers. All those fancy variables created around the target individuals have many holes in them, for various reasons. Maybe they are new customers, or they just browsed a few items but never bought anything yet. Some customers may have shopped only in certain categories, but stayed away from others. Some customers may have been very diligent in deleting their online trails. To do the personalization properly and consistently, we need to fill in such gaps.

Most of personalization engines, unfortunately, are designed to act only on available (largely, “known”) data. When marketers go too far with what’s known to them, the customers who casually let some parts of their lives known to marketers get bombarded with the same messages until they get completely sick of them. That is a sad situation as, categorically speaking, people with known behaviors often account for less than – at times far less than – 5 percent of the approachable universe. So, in that scenario, 5 percent get to be stalked, while 95 percent are ignored. Not ideal at all.

Enter statistical modeling. I have been emphasizing the importance of statistical modeling even in the data-rich environment, because we will never know everything about everyone, and statistical modeling systematically converts “unknowns” to “potentials.” No, we may not know for sure that a particular target is indeed a “gardening enthusiast” (and no, buying just “one” garden hose nozzle may not be enough). But yes, we can say that she is “very likely to be” a gardening enthusiast, with statistical techniques effectively mining available data — such as what other products she purchased and browsed with varying frequencies and intervals. The results of the models are “scores” by which you can measure the degree of confidence, as in a nine out of a 10 scale. This is much simpler than having to worry about hundreds of variables with more holes Swiss cheese.

Building a customer-centric view and filling in the gaps with modeling techniques is far more superior to a personalization engine that would just ingest unrefined SKU-level data. For one, we don’t get to bother people just because we had a glimpse of certain behavior, as statistical modeling considers hundreds, if not thousands, of variables around the person. Secondly, having “potential” values for certain behavior enables marketers to act on most of the targets, not just fractions of them. Going further, we can even estimate channel and timing preference in addition to what we often call “personas” or propensity scores.

The result of modeling work will make the personalization engines run better, too. After all, those software solutions are designed to ingest any type of variable. And the model scores – which are summaries of hundreds of data points – look just like another set of variables, anyway. Consider such scores without any missing values as really tasty coffee beans that you can put into your shiny espresso machine.

Country store owners in the old days were known to have personal touches, because they treated their customers as people. (Well, of course without being creepy. Refer to “Don’t Do It Just Because You Can”). They would not have offered more hammers to you just because you just purchased a hammer. They would have put it in context (i.e., human touch), and then they would have suggested products that you may benefit from. (As in “Hey, don’t you need protective gloves, too? I know you’re a klutz!”)

Now we have access to enough technology, mathematical skills and data to do such personalized marketing to millions of people at a time. But it will work only if marketers do not lose sight of what matters, and commit to the proper way, preparing the data specifically for personalization efforts and programming personal touches into algorithms. Technology made things easy for us, but it is equally easy to abuse it. And let’s not forget that we are just personally abusing other human beings when we abuse technology and data. We’d better not call that personalization.

5 Elements to Move From Segmentation to Personalization

There is a big difference between segmentation and personalization. Most marketers do segmentation pretty well — they use some sort of marketing database or CRM system to identify audience members who will receive an outbound campaign. Sometimes a particular segment is identified as a “persona,” where a description of a fictional member of the segment is used to gain clarity and consensus among the teams — and to help align content that will best resonate.

However, segmentations alone are static, because they are based on a marketing calendar. They solve yesterday’s marketing challenge. People don’t interact with brands along a calendar. They interact across channels and in non-linear methods based on a multitude of stimuli — many of which are not controlled by the brand. Personalization is an additional layer on top of segmentation to ensure that campaigns are responsive to audience behaviors and relevant to their needs.

To move from effective segmentation to engaging personalization, there are five elements to consider:

1. Dynamic Targeting. Our multi-device browsing habits and constantly shifting interests will break traditional segmentation models. Broad segments like “gadget enthusiasts” or “Millennial” will no longer work. We must use the automation technology to create dynamic and agile models that can adapt to behaviors as they occur, and personalize the content at the individual level, not the segment or campaign level. Our goal is to target individuals based on a collection of identifiable characteristics and behaviors, rather than targeting a collection of individuals who share characteristics. As I’ve often said in planning meetings, “The way customers act, you’d think each one was a different person!”

2. Effective Content. Content must sell. Always. Too much of the marketing content out there is merely interesting. That has value, except it isn’t realistic to think that anyone has time to read everything that is interesting. We’ve got to set the bar higher. Content must be viewed as part of (or at least aligned to) the product – it must help solve the same problems that the product solves. This includes re-thinking how offline media assets get turned into digital experiences. For example, break up a TV spot into dozens of snackable visual elements with hyperlocal additions for price, store locations and availability. That turns content into commerce, and brings our content marketing closer to the analytics-driven marketing that drives revenue.

3. Revisit Cross-Channel Data Collection. While still complicated, cross-channel data is starting to become more actionable. A big part of the early success has nothing to do with technology, but with people. CMOs are realizing that there are efficiencies and opportunities if all channels are working toward the same goal, with the same view of the customer. To improve the maturity of these solutions, we will likely need more experimentation around both data collection and insights-driven campaign management. This unified, actionable view of the customer is still ahead of most companies, but most of us can get started by combining channels at key moments of the lifecycle.

4. Customer-level Media. Several campaign management vendors are tackling the challenge of analysis and customer engagement across owned, earned and paid media. Ask your vendors about their plans for improving customer journey mapping. Traditional linear customer journey maps are obsolete — you need technology to help you dynamically associate content and campaigns across the myriad individual experiences. At the same time, media buying is increasingly at the customer level, via hashtag identification and CRM-based targeting. Bring your own data to Facebook and Twitter, and find your key audiences across the Web — in the context that makes sense for your product and brand. 2015 will continue to see integrations through the various data services platforms (DSPs) and ad retargeting programs.

5. Brand Promise Ubiquity. Every interaction with a brand must live up to the brand promise – not just the how and what we do, but the why we do it. Marketing data can’t stay in the marketing department; it’s got to be utilized throughout the organization. Marketers are more curators of experience than controllers (broadcasters) of message, and our brand promise must be the banner under which every employee, agency and partner interacts with people. A recent study by research and consulting firm Software Advice found that using big data to match sales and service reps to the individual needs and personalities of customers will significantly increase satisfaction, call efficiency and sales revenue. That is just one example of how marketing must collaborate with other departments to optimize customer experience across every touchpoint.

How are you using integrated data to turn your segmentation strategy into a personalization strategy? Share your thoughts in the comments below.

Wasted Personalization

Chatting with a friend about this article, he suggested I write about the most memorable email I’ve received. And while that would be interesting, I know I find emails memorable for reasons you might not. I’m most enthralled by the development, design or concept, whereas you might be most taken by the message 

Chatting with a friend about this article, he suggested I write about the most memorable email I’ve received. And while that would be interesting, I know I find emails memorable for reasons you might not. I’m most enthralled by the development, design or concept, whereas you might be most taken by the message.

As my friend described the most memorable email he had received, I thought about why that same email would have been memorable to me. That led me down the path I started in my last article—applying direct-mail lessons to today’s email campaigns.

In 1995, I founded The World-Wide Power Company, the world’s first international distributor of all (graphics) extension-based technology. As the only distributor of all things plug-in, we had extensive records about who owned what, their core applications, versions, numbers of copies and so much more. Back then, this data lived in our invoicing system, which suited us perfectly as we had customized FileMaker Pro for inventory, invoicing, reporting, vendor tracking, managing the product matrix, and other day-to-day business activities. This meant our customer and purchase data were clicks away any time we built a direct-mail campaign.

Our most-successful campaigns were our weekly direct-mail postcards and letters, nicknamed PUN (product-upgrade notice) and CUN (competitive-upgrade notice). These events were mailed each week to everyone in our quarter-million name database who owned a product undergoing an upgrade during the week or for which a competitive product had been announced. The messaging on the cards went something like this (I’ve represented some of the messaging with the field names from our database, shown in all caps, to save space and to illustrate connections to fields):

CUSTOMER NUMBER: [001097]

[LESLIE STRONGMAN], [XYZ PRINTING]
OR THE CURRENT IS/IT DIRECTOR OR GRAPHICS MANAGER

[ 1/22/1997 216350 1 Imposer XTension]
[ 4/4/1997 221450 5 Imposer XTension]
[ 4/7/1997 221527 2 Imposer XTension/MarkIt Bundle]

Dear [Leslie],

On behalf of [XYZ Printing], you purchased [Imposer] from The World-Wide Power Company. Your purchase information, including invoice date, invoice number, and quantity, is listed above. According to our records, you currently own [8 copies] of QuarkXPress [4]. In order to upgrade your [QuarkXPress] to version [5] and maintain the ability to [SHORT DESCRIPTION], you must also upgrade your [XTENSIONS] purchases listed above.

[Imposer 2.0] has been upgraded to provide
[1-LINE BENEFIT] and to support [QuarkXPress 5].

[LIST BENEFITS]

[LIST FEATURES]

[Imposer Pro] retails for [$399]. For a limited time, upgrade each of your copies of [Imposer 1.X] or [2.X] to [Imposer Pro] for [$199].

Call ThePowerXChange to upgrade and take advantage of this special pricing before [31 March 2003]. Prices do not include taxes, where applicable, or shipping. Delivery options are as follows: [electronic delivery is free] or [CD-ROM sent via Airborne overnight for $12].

The response rates from these postcards averaged more than 50 percent, but our best result was more than 80 percent! This is a number to make any marketer salivate.

Having set the stage, the reason I bring this up is to discuss the opportunities lost by today’s marketer—even me, and I most certainly know better.

Today, personalization is demonstrated in our emails often by including the recipient’s first name in the greeting or subject line, but rarely, very rarely, do we see the level of personalization I’ve shown above—except perhaps in the case of our shopping cart abandonment messages … but that’s actually my point.

We know abandonment messages enjoy high open and click rates and yet we don’t apply the trigger of those messages to our everyday marketing messages. Why not? Difficult? Lack of technical know how? Lack of resources? All of the above? Probably.

Step back and ascertain a complete view of the data you have within your organization—and I’m not talking about big data here. Look to your accounting system and ferret out nuggets like those in my example. Look to your marketing database and find unusual bits to help you connect with the recipient. See if your badge scans can disclose something new, or if your sales team can add color.

What my friend told me about his most memorable email (from Hyatt) was the message thanked him for having stayed at a Hyatt property 75 times and provided the name and location of his first Hyatt stay. He enjoyed the trip down memory lane, and while he admits most of the information was wrong, he still felt a strong connection and fondness for Hyatt because they remembered him.

As with all things marketing, this could turn out poorly for the marketer if recipient’s recollections bring back unpleasant memories, but that’s simply a marketing risk we take every day.

The next time you send out a marketing message, consider if you’re wasting personalization on a simple greeting and see if it’s possible to take it to the next level by including something memorable, important, funny, or, well, personal, that can actually connect with your audience.

“Dear David … Oops, I Mean Carolyn” – Blunders in Marketing Automation

Ah, the blunders of automation. In the “old” days, when direct mail personalization was the shiny new penny, there were critical quality control procedures in place to ensure the right personalization data was being ink-jetted/lasered onto the right creative package/offer. We made sure the address data in each record matched the personalized salutation and output on the order device. Now that the email world has collided with database automation, QC efforts seem to be non-existent. As a customer, I’m insulted. As a marketer, I’m embarrassed for our entire industry.

Ah, the blunders of automation.

In the “old” days, when direct mail personalization was the shiny new penny, there were critical quality control procedures in place to ensure the right personalization data was being ink-jetted/lasered onto the right creative package/offer. We made sure the address data in each record matched the personalized salutation and output on the order device.

Now that the email world has collided with database automation, QC efforts seem to be non-existent. As a customer, I’m insulted. As a marketer, I’m embarrassed for our entire industry.

I first noticed the problem about 8 years ago when I got an email that started “Dear First Name”—it took everything I had to not choke on my morning latte. “Hmmm,” I thought, “somebody’s going to get fired for this one.”

Apparently, this “somebody” packed their bags and got a job managing email at yet another company, because their next email faux pas was an email personalized to me, but read, “Since you live in Arkansas …” Really? I don’t think I’ve ever even visited Arkansas, so either you’ve got the wrong Carolyn, or your data is really, really bad.

Or how about those sales people who look like they’re sending a 1:1 email, but the results have gone completely awry? The sender is so lazy, they’ve clearly just cut and pasted different text together—different fonts, different font colors, different font sizes.

My favorite one started, “Dear Carolyn, We get it too!” Huh? Did we meet and have a conversation about something and I dozed off?

Roger Connors, Author, Co-CEO/Co-President, Partners In Leadership at Ozprinciple.com, emails me regularly with a “Dear David” salutation. Absolutely no idea who these folks are, why I’m on their email list and why they think my name is David. And it seems Roger isn’t trusted to email by himself because his emails always come from Roger Connors and Tom Smith. Who is Tom and why won’t he let Roger send out an email alone? Perhaps because he’s never accurate with the name of his target? Nice QC supervision there, Tom.

Other organizations seem to get my name right, but miss the mark on personalization within the body copy of the email. Take this one from the Director of Retail Sales at Dixon Ticonderoga Company, who emails, “I hope to meet privately you and others to discuss the options you offer for building a non-traditional marketing strategy for .” Wow.

Then, there’s the Subject line. One of my favorites? Subject: “=?utf-8?Q??=Carolyn, Are You Right on Time, Right on Target?”

And let’s not forget those images that don’t download properly, so I’m looking at a big box with an “X” in the middle of it. Or how about links that don’t work (spinning … spinning … spinning … ) or link to a page that has nothing to do with the content in the email?

Or better yet, really examine your copy to make sure you’re not insulting anyone. The one I received this morning that reads: “Younger is better. Marketers need new technologies …To keep customers happy … To make numbers … To keep u p… Old technologies are clunky. Non-agile. Old technologies are old. Like our fellow Chi-town native, Kanye, we don’t like it unless it’s brand new.”

Hey, I may not be a spring chicken, that just rubbed me the wrong way.

So here’s a tip for all you marketers that use email in your mix: Set up your email campaign and then blast to test names in your campaign list (use a variety of email accounts so you can see how the email renders after passing through gmail, AOL and other email servers). QC it. Fix it. Send a test email again. QC it. Fix it. Send a test email again. Repeat until everything is perfect, because your first brand impression may be your last.